National Level
Row
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Row
Map 2006
Map 2010
Map 2015
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---
title: "Homicides in Mexico 2006-2015"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: scroll
social: menu
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
knitr::opts_chunk$set(echo = TRUE)
```
```{r, include=FALSE}
library(raster)
library(sp)
library(rgdal)
library(readr)
library(tidyverse)
library(crosstalk)
library (plotly)
library(dplyr)
library(ggplot2)
library(rasterVis)
library(doBy)
library(sp)
library(maptools)
if (!require(gpclib)) install.packages("gpclib", type="source")
gpclibPermit()
library(forcats)
library(ggmap)
library(viridis)
library(stringi)
library(flexdashboard)
library(shiny)
```
```{r, include=FALSE}
hom<- read_csv("perfiles_homicidios.csv")
hom2<-read_csv("perfiles_homicidios.csv")
hom$edo_code[hom$edo_code=="01" ] <- "1"
hom$edo_code[hom$edo_code=="02" ] <- "2"
hom$edo_code[hom$edo_code=="03" ] <- "3"
hom$edo_code[hom$edo_code=="04" ] <- "4"
hom$edo_code[hom$edo_code=="05" ] <- "5"
hom$edo_code[hom$edo_code=="06" ] <- "6"
hom$edo_code[hom$edo_code=="07" ] <- "7"
hom$edo_code[hom$edo_code=="08" ] <- "8"
hom$edo_code[hom$edo_code=="09" ] <- "9"
unique(hom$edo_code)
dir_main = "/Users/eves/Documents/"
dir_adm = paste(dir_main,"Spring 2018/Data Viz/Visual experiment/mbaprgw", sep="")
mex_shp1 <- readOGR(dsn = dir_adm, layer = "mbaprgw")
hom$edo_code <- as.numeric(hom$edo_code)
hom$pob <- as.numeric(hom$pob)
```
```{r, include=FALSE}
#Theme functions for maps and color map
#source: https://timogrossenbacher.ch/2016/12/beautiful-thematic-maps-with-ggplot2-only/
theme_map <- function(...) {
theme_minimal() +
theme(
text = element_text(family="Helvetica", size = 10, color = "#22211d"),
axis.line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
#panel.grid.minor = element_line(color = "#ebebe5", size = 0.2),
panel.grid.major = element_line(color = "#ebebe5", size = 0.2),
panel.grid.minor = element_blank(),
#plot.background = element_rect(fill = "#f5f5f2", color = NA),
#panel.background = element_rect(fill = "#f5f5f2", color = NA),
#legend.background = element_rect(fill = "#f5f5f2", color = NA),
panel.border = element_blank(),
...
)
}
```
National Level
=======================================================================
Row {data-width=700}
-----------------------------------------------------------------------
```{r, echo=FALSE}
hom2 %>%
filter(sex == "Ambos", escolaridad == "Todos", rango_edad == "Todos", edo_code == "Todos") %>%
plot_ly( x = ~year, y = ~homtot, type = "bar") %>%
layout(title ="Number of Homicides per year")
```
Row {data-width=700}
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Row {.tabset .tabset-fade}
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### Map 2006
```{r, echo=FALSE}
######## 2006
hom_6 <-
hom %>%
filter(year == "2006", sex == "Ambos", escolaridad == "Todos", rango_edad == "Todos", !edo_code == "Todos")
hom_6$edo_code <- as.numeric(hom_6$edo_code)
mex_shp1_fort <- fortify(mex_shp1, region="COV_ID") %>%
mutate(id=as.numeric(id))
map_data_6<- mex_shp1_fort %>%
left_join(hom_6, by = c("id"= "edo_code"))
#Map
no_classes <- 5
labels <- c()
quantiles_6<- quantile(map_data_6$thom,
probs = seq(0, 1, length.out = no_classes + 1), na.rm = TRUE)
labels <- c()
for(idx in 1:length(quantiles_6)){
labels <- c(labels, paste0(round(quantiles_6[idx], 2),
" – ",
round(quantiles_6[idx + 1], 2)))
}
labels <- labels[1:length(labels)-1]
map_data_6$Hom_edo_quantiles <- cut(map_data_6$thom,
breaks = quantiles_6,
labels = labels,
include.lowest = T)
map_data_6_p<- ggplot() +
# polygons with data
geom_polygon(data = map_data_6, aes(fill = Hom_edo_quantiles,
x = long,
y = lat,
group = group)) +
# rayon outline
geom_path(data = map_data_6, aes(x = long,
y = lat,
group = group),
color = "grey", size = 0.1) +
# for projection
coord_equal() +
# add the previously defined basic theme + color
theme_map() +
# labels
labs(x = NULL,
y = NULL,
title = "Homicide Rate in 2006 ",
subtitle = "Homicide rate per 100,000 inhabitants",
caption = "Source: Data Civica") +
theme(legend.position = "bottom") +
scale_fill_viridis(
option = "magma",
discrete = T,
direction = -1,
name = "",
guide = guide_legend(
keyheight = unit(3, units = "mm"),
keywidth = unit(6, units = "mm"),
title.position = 'top',
reverse=F,
title.hjust = 0.5,
label.hjust = 0.5
))
#p<-ggplotly(map_data_6_p) %>%
# highlight(
# "plotly_hover",
# selected = attrs_selected(line = list(color = "black"))
#) %>%
# widgetframe::frameWidget()
#p
map_data_6_p
```
###Map 2010
```{r, echo= FALSE}
hom_10 <-
hom %>%
filter(year == "2010", sex == "Ambos", escolaridad == "Todos", rango_edad == "Todos", !edo_code == "Todos")
hom_10$edo_code <- as.numeric(hom_10$edo_code)
mex_shp1_fort <- fortify(mex_shp1, region="COV_ID") %>%
mutate(id=as.numeric(id))
map_data_10<- mex_shp1_fort %>%
left_join(hom_10, by = c("id"= "edo_code"))
#Map
no_classes <- 5
labels <- c()
quantiles_10<- quantile(map_data_10$thom,
probs = seq(0, 1, length.out = no_classes + 1), na.rm = TRUE)
labels <- c()
for(idx in 1:length(quantiles_10)){
labels <- c(labels, paste0(round(quantiles_10[idx], 2),
" – ",
round(quantiles_10[idx + 1], 2)))
}
labels <- labels[1:length(labels)-1]
map_data_10$Hom_edo_quantiles <- cut(map_data_10$thom,
breaks = quantiles_10,
labels = labels,
include.lowest = T)
map_data_10_p<- ggplot() +
# polygons with data
geom_polygon(data = map_data_10, aes(fill = Hom_edo_quantiles,
x = long,
y = lat,
group = group)) +
# rayon outline
geom_path(data = map_data_10, aes(x = long,
y = lat,
group = group),
color = "grey", size = 0.1) +
# for projection
coord_equal() +
# add the previously defined basic theme + color
theme_map() +
# labels
labs(x = NULL,
y = NULL,
title = "Homicide Rate in 2010 ",
subtitle = "Homicide rate per 100,000 inhabitants",
caption = "Source: Data Civica") +
theme(legend.position = "bottom") +
scale_fill_viridis(
option = "magma",
discrete = T,
direction = -1,
name = "",
guide = guide_legend(
keyheight = unit(3, units = "mm"),
keywidth = unit(6, units = "mm"),
title.position = 'top',
reverse=F,
title.hjust = 0.5,
label.hjust = 0.5
))
map_data_10_p
#p<- ggplotly(map_data_15_p) %>%
# highlight(
# "plotly_hover",
# selected = attrs_selected(line = list(color = "black"))
#) %>%
# widgetframe::frameWidget()
#p
```
###Map 2015
```{r, echo= FALSE}
hom_15 <-
hom %>%
filter(year == "2015", sex == "Ambos", escolaridad == "Todos", rango_edad == "Todos", !edo_code == "Todos")
hom_15$edo_code <- as.numeric(hom_15$edo_code)
mex_shp1_fort <- fortify(mex_shp1, region="COV_ID") %>%
mutate(id=as.numeric(id))
map_data_15<- mex_shp1_fort %>%
left_join(hom_15, by = c("id"= "edo_code"))
#Map
no_classes <- 5
labels <- c()
quantiles_15<- quantile(map_data_15$thom,
probs = seq(0, 1, length.out = no_classes + 1), na.rm = TRUE)
labels <- c()
for(idx in 1:length(quantiles_15)){
labels <- c(labels, paste0(round(quantiles_15[idx], 2),
" – ",
round(quantiles_15[idx + 1], 2)))
}
labels <- labels[1:length(labels)-1]
map_data_15$Hom_edo_quantiles <- cut(map_data_15$thom,
breaks = quantiles_15,
labels = labels,
include.lowest = T)
map_data_15_p<- ggplot() +
# polygons with data
geom_polygon(data = map_data_15, aes(fill = Hom_edo_quantiles,
x = long,
y = lat,
group = group)) +
# rayon outline
geom_path(data = map_data_15, aes(x = long,
y = lat,
group = group),
color = "grey", size = 0.1) +
# for projection
coord_equal() +
# add the previously defined basic theme + color
theme_map() +
# labels
labs(x = NULL,
y = NULL,
title = "Homicide Rate in 2015 ",
subtitle = "Homicide rate per 100,000 inhabitants",
caption = "Source: Data Civica") +
theme(legend.position = "bottom") +
scale_fill_viridis(
option = "magma",
discrete = T,
direction = -1,
name = "",
guide = guide_legend(
keyheight = unit(3, units = "mm"),
keywidth = unit(6, units = "mm"),
title.position = 'top',
reverse=F,
title.hjust = 0.5,
label.hjust = 0.5
))
map_data_15_p
#p<- ggplotly(map_data_15_p) %>%
# highlight(
# "plotly_hover",
# selected = attrs_selected(line = list(color = "black"))
#) %>%
# widgetframe::frameWidget()
#p
```
State Level
=======================================================================
Row {data-width=600}
-----------------------------------------------------------------------
### Homicides per state
```{r, echo=FALSE}
table_homicides_state <- hom2 %>%
filter(sex == "Ambos", escolaridad == "Todos", rango_edad == "Todos", !edo_code == "Todos") %>%
ggplot(aes(x = edo_nom, y = homtot)) + geom_col(aes(fill = year)) + facet_wrap(~year) + theme_minimal() + theme(axis.title = element_blank(), axis.text.x=element_blank()) + theme(legend.position = "none") + ggtitle("Total Homicides in Mexico per State")
table_homicides_state = ggplotly(table_homicides_state)
table_homicides_state
```
Row
-----------------------------------------------------------------------
### Evolution of Homicides per state
```{r, fig.width=10, fig.height=7, echo=FALSE}
#Geomline per title
# Source code: http://rpubs.com/cpsievert/275511 , we add modifications to the code
hom_total <- hom2 %>%
filter(sex == "Ambos", escolaridad == "Todos", rango_edad == "Todos", !edo_code == "Todos")
hom_total$year <- as.Date(as.character(hom_total$year), format = "%Y")
brks <- hom_total$year[seq(1, length(hom_total$year))]
lbls <- lubridate::year(brks)
h <- SharedData$new(hom_total)
g_hom <- ggplot(h) +
geom_line(aes(year, thom, group = edo_nom)) +
ggtitle("Filter a state")+
labs(y="Homicide rate")+
theme_bw()
filter <- bscols(
filter_select("id", "Select a state", h, ~edo_nom),
ggplotly(g_hom, dynamicTicks = TRUE),
widths = c(15,15)
)
h2 <- SharedData$new(hom_total, ~edo_nom, "Select a state")
g_hom <- ggplot(h2) +
geom_line(aes(year, thom, group = edo_nom)) +
ggtitle("Compare between states")+
labs(y="Homicide rate")+
theme_bw()
select <- highlight(
ggplotly(g_hom, tooltip = "State"),
selectize = TRUE, dynamic = TRUE, persistent = TRUE
)
x<-bscols(filter, select)
x
```
Gender
=======================================================================
Row
-----------------------------------------------------------------------
### Total numbers of deaths
```{r, echo=FALSE}
total<- hom_total%>%
summarise(sum(homtot))
```
```{r, echo=FALSE}
valueBox("195,909", icon = "person", color = "black")
```
```{r, echo=FALSE}
#Total Homicides MALE
hom_male <-hom2 %>%
filter(sex == "Hombre", escolaridad == "Todos", rango_edad == "Todos", edo_code == "Todos")
total_male <- hom_male%>%
summarise(sum(homtot))
```
### Male homicides
```{r, echo=FALSE}
valueBox("173,936", icon = "fa-male", color = "aqua")
```
```{r, echo=FALSE}
#Total Homicide FEMALE
hom_female <- hom2 %>%
filter(sex == "Mujer", escolaridad == "Todos", rango_edad == "Todos", edo_code == "Todos")
total_female<- hom_female%>%
summarise(sum(homtot))
```
### Female homicides
```{r, echo=FALSE}
valueBox("21,045", icon = "fa-female", color = "orange")
```
Row
-----------------------------------------------------------------------
```{r, echo=FALSE}
hom_male <-hom2 %>%
filter(sex == "Hombre", escolaridad == "Todos", rango_edad == "Todos", edo_code == "Todos")
colnames(hom_male)[8] <- "thom_hom"
hom_male_t<-hom_male %>%
select("year", "thom_hom")
hom_female <- hom2 %>%
filter(sex == "Mujer", escolaridad == "Todos", rango_edad == "Todos", edo_code == "Todos")
colnames(hom_female)[8] <- "thom_female"
hom_female_t<-hom_female %>%
select("year", "thom_female")
hom_tot_t<- hom_female_t %>%
left_join(hom_male_t, by ="year")
hom_tot_t$thom_female <-as.numeric(hom_tot_t$thom_female)
hom_tot_t$thom_hom <-as.numeric(hom_tot_t$thom_hom)
hom_tot_t$year <- as.Date(as.character(hom_tot_t$year), format = "%Y")
library(lubridate)
# labels and breaks for X axis text
brks <- hom_tot_t$year[seq(1, length(hom_tot_t$year))]
lbls <- lubridate::year(brks)
# plot
p<-ggplot(hom_tot_t, aes(x=year)) +
geom_line(aes(y=thom_female, col="Female rate")) +
geom_line(aes(y=thom_hom, col="Male rate")) +
labs(title="Time Series of Homicites per 100,000 inhabitants",
colour ="Gender group",
subtitle="National level, from 2006 to 2011",
caption="Source: Data Civica", y="Homicide rate") +
scale_x_date(labels = lbls,
breaks = brks)
p = ggplotly(p)
p
```